Scene reconstruction using MRF optimalization with image content, adaptive energy functions

Ping Li, R. Klein Gunnewiek, P.H.N. With, de

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

Multi-view scene reconstruction from multiple uncalibrated images can be solved by two stages of processing: first, a sparse reconstruction using Structure From Motion (SFM), and second, a surface reconstruction using optimization of Markov random field (MRF). This paper focuses on the second step, assuming that a set of sparse feature points have been reconstructed and the cameras have been calibrated by SFM. The multi-view surface reconstruction is formulated as an image-based multi-labeling problem solved using MRF optimization via graph cut. First, we construct a 2D triangular mesh on the reference image, based on the image segmentation results provided by an existing segmentation process. By doing this, we expect that each triangle in the mesh is well aligned with the object boundaries, and a minimum number of triangles are generated to represent the 3D surface. Second, various objective and heuristic depth cues such as the slanting cue, are combined to define the local penalty and interaction energies. Third, these local energies are adapted to the local image content, based on the results from some simple content analysis techniques. The experimental results show that the proposed method is able to well the preserve the depth discontinuity because of the image content adaptive local energies.
Original languageEnglish
Title of host publicationAdvanced concepts for intelligent vision systems : 10th international conference, ACIVS 2008, Juan-les-Pins, France, October 20-24, 2008 ; proceedings
EditorsJacques Blanc-Talon, Salah Bourennane, Wilfried Philips
Place of PublicationBerlin
PublisherSpringer
Pages872-882
ISBN (Print)978-3-540-88457-6
DOIs
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
Volume5259
ISSN (Print)0302-9743

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